class_docstr: 自定义数据集
code: def __init__(
      self, images_path: list, images_class: list, transform=None
  ):
      self.images_path = images_path
      self.images_class = images_class
      self.transform = transform
  
      delete_img = []
      for index, img_path in tqdm(enumerate(images_path)):
          img = Image.open(img_path)
          w, h = img.size
          ratio = w / h
          if ratio > 10 or ratio < 0.1:
              delete_img.append(index)
              # print(img_path, ratio)
  
      for index in delete_img[::-1]:
          self.images_path.pop(index)
          self.images_class.pop(index)
  
file_path: deep-learning-for-image-processing/pytorch_classification/tensorboard_test/my_dataset.py
imports: from tqdm import tqdm
  from PIL import Image
  import torch
  from torch.utils.data import Dataset
label_desc: Use image node_id 1 to create a new MyDataSet object from inherited base classes: Dataset with example: obj = MyDataSet(images_path, images_class, transform)